6 papers
Anderson Acceleration for Distributed Constrained Optimization over Time-varying Networks
Haijuan Liu, Xuyang Wu
This paper applies the Anderson Acceleration (AA) technique to accelerate the Fenchel dual gradient method (FDGM) to solve constrained optimization problems over time-varying netwo…
Historical Information Accelerates Decentralized Optimization: A Proximal Bundle Method
Zhao Zhu, Yu-Ping Tian, Xuyang Wu
Historical information, such as past function values or gradients, has significant potential to enhance decentralized optimization methods for two key reasons: first, it provides r…
Globally-Constrained Decentralized Optimization with Variable Coupling
Dandan Wang, Xuyang Wu, Zichong Ou +1
Many realistic decision-making problems in networked scenarios, such as formation control and collaborative task offloading, often involve complicatedly entangled local decisions,…
A Unified Dual Consensus Approach to Distributed Optimization with Globally-Coupled Constraints
Zixuan Liu, Xuyang Wu, Dandan Wang +1
This article explores distributed convex optimization with globally-coupled constraints, where the objective function is a general nonsmooth convex function, the constraints includ…
Asynchronous Distributed Optimization with Delay-free Parameters
Xuyang Wu, Changxin Liu, Sindri Magnusson +1
Existing asynchronous distributed optimization algorithms often use diminishing step-sizes that cause slow practical convergence, or use fixed step-sizes that depend on and decreas…
Achieving violation-free distributed optimization under coupling constraints
Changxin Liu, Xiao Tan, Xuyang Wu +2
Constraint satisfaction is a critical component in a wide range of engineering applications, including but not limited to safe multi-agent control and economic dispatch in power sy…